Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope.
Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope.
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DOI：
10.1109/embc44109.2020.9175858
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发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Ramanujam N
中科院分区：
文献类型：
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作者：
Asiedu MN;Skerrett E;Sapiro G;Ramanujam N
我们将特征提取和机器学习方法应用于白色袖珍阴道镜的多种对比源（乙酸、卢戈碘和绿色光），这是一种用于宫颈癌筛查的低成本护理点阴道镜。我们结合对比源的联合收割机特征，并分析添加每种对比后的诊断改进。我们发现，与仅使用一种来源相比，使用其他造影剂时总体AUC增加。
We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol’s iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additional contrast agents compared to using only one source.